Which Tasks Should You Outsource to Grow as a Top Real Estate Data Aggregator?

Main 5 Challenges That Real Estate Data Aggregators Face

Property data-associated tasks have exponentially increased with the beginning of omnichannel data and big data accessibility. Deeper exposure to trends and patents in real estate has become possible. And data aggregators can’t afford to overlook customer requirements for non-traditional data.

Keep Data Very Comprehensive

Omni-channel data capturing is the primary challenge for any real estate data aggregator. Having privacy law acquiescence on third-party cookies and customers rightly aware of their data, collecting zero, first-party, and second-party third-party data has become very difficult.

Maintenance of Data Accuracy

Imprecise and incomplete data creates a distinctive challenge for any data aggregators. Credibility suffers if clients get data that lacks authenticity or is outdated. To overwhelm this, aggregators have to check active public records: continuously

• Parcel datasets – neighborhood
• Real estate listing database
• Real estate tax database
• Real estate transaction database

It is hard to ensure that data scraped from readily accessible and unclear public resources is permanently changed into a well-structured and readily available format. Managing, cleansing, validating, and regularizing data from structured and unstructured sources becomes challenging when following deadlines.

Keeping Illegal Data in Control

Data aggregators utilize real-time property data as the force multiplier for supporting their clients having deep properties and shopper intelligence. However, it has one problem - dirty data. Dirty data demonstrates inaccuracies, duplicate data, and other faults in different forms. Stagnant and inaccurate data are not dependable enough to display if the property title is entirely free from liens, claims, or other problems, and the workload certainly increases.

Data Decay Prevention

Keeping all the dynamic data traits refreshed, updated, and comprehensive makes challenges for any real estate data aggregators. That includes essential data like mortgage, tax, property titles, appraisals, etc. Selling customers outdated, incomplete, or inaccurate property data can threaten a real estate data company's revenue streams and brand credibility.

Use of Smart Tools for Leveraging Data Attributes

The inability to advance and index real estate data points and restore them is a difficult challenge for real estate data aggregators. Lacking innovative tools and capabilities makes that more difficult. It is difficult to recognize, mix-match, and accumulate attributes, including mortgage, property data, homeowner data, and property document images. And no single tool is there for all the tasks.

In addition, standard data like owner’s identification, prices, and transactions are there for more data types in real estate. They might include geospatial data and plot shape, the proximity of flammable vegetation, or social media rating about nearby facilities.

Insurers, builders, investors, and other stakeholders of the property sector can use this data using new tools.

Tasks to Outsource If You are Real Estate Data Aggregators

Real estate data aggregators gather property-associated details from different sources, validate data and make that accessible to MLS websites, agents, contractors, insurers, and stakeholders in this industry. Therefore, they outsource well-structured jobs and concentrate on in-house resources for growth. The jobs they outsource to get higher productivities include:

1. Acquisition of Property Data

Most real estate data is not easily available on digital resources and still rests in leases, licenses, paper deeds, agreements, and documents.

Outsourced data attainment of 110 million property records has helped the leading property portal help 200 million online customers easily search for apartment and home listings and buy mortgages.

Joint undertakings in property data attainment include:

• Collecting real estate data from obscure and non-traditional sources
• Creating a comprehensive property data repository
• Finding property tax data from county, state, or sub-taxing websites
• Getting authentic property data resources, both local and global
• Making an ongoing real estate data acquisition procedure or cycle
• Real-time raw data gathering from public records and other resources like newspapers

2. Property Data Standardization

An MLS site or property consultant from the UK improved its brand reliability and customer experience by outsourcing property data projects. A company capable of leveraging Google Street View and Google Maps gathered and standardized formless property data from various agencies and brochures.

In the case of different listing services where data gets assumed to become structured, hundreds of sources are there with their ways of structuring data without standardization. Even when such data is accessible in a wholly digital format, participating listings from numerous resources become complex and need time.

Joint projects in property data standardization include:

• Address normalization
• Assigning geographic indicators and Zip codes wherever possible
• Data normalization
• Geographic data normalization (city, country, state, county, etc.)

3. Rule-based Real Estate Data Verification and Validation

Validation of property data is crucial from an accuracy angle, data hygiene, and compliance for analytical utilities. Both rule-based and manual property data verification and validation are utilized for this objective.

Outsourcing verification and validation of more than 10 million records assisted a US-based publisher of property periodicals rise circulation and improving credibility by offering accurate insights and subscribers’ data.

General data validation jobs include:

• Agent Data Validation
• Demographic Validation
• GIS Data Validation
• IP Address Validation
• Mobile & Email Validation
• Postal Address Validation
• Property Data Validation
• Reverse Phone Lookup

4. Property Data Enhancement

Both conventional and non-conventional real estate data needs have amplified the workload of real estate aggregators. Property datasets might need improvement with new data like crime statistics, weather patterns, building footprint, traffic statistics, etc.

General data improvement tasks include:

• Add-on of images and additional attachments
• Commercial, financial, residential, rental, and geospatial information
• Demographic, sociographic, and other applicable data in the public records
• Identification of lost fields, the addition of applicable values, and reach of a nil missing-value status.
• Satisfying cross-property fields using data from different MLS listings

5. Constant List Updates

The danger of data deterioration looms over the operations of all real estate data aggregators. Therefore, they use automated systems making constant data cycles to validate, update, and improve property lists.

Property outsourcing services have set an independent data procedure to search and gather property data from over 1,350 listing services. This helped a New York-based property portal offer customers an easy-to-use database across different counties.

Luckily, most present real estate data is digitally and publicly available to aggregate real estate and commercial data to automate data monitoring abilities. They can set dependable systems to find any information changes or outliers on the digital horizon.

General tasks related to constant listing updates include:

• Accurate and ongoing property list management
• Automated tracking with monitoring of variations in current data
• Capture and update price fluctuations or asset valuation
• Deploy technology improvements to grow gainfully
• Leveraging business rules and macros to update a database with applicable transactional values within real-time
• Removal of obsolete and redundant data from a database occupied

AI and ML models are transforming the real estate business. ML algorithms assist realtors in assessing and noting key property features in particular areas which are expected to affect a selling price. AI's openings are already helping listing websites to know and separate data for safeguarding sales.

All these need skilled resources that are hard to manage internally. These tasks consume time and require more diligence and attention; therefore better outsourced to real estate data management professionals.

Conclusion

To be competitive, real estate data aggregators have to use a massive amount of accessible and dissimilar data and always remain in need of high-quality support. So, real estate data aggregators must outsource complex tasks to property support services. This helps them ensure business growth by freeing up main resources and utilizing them anywhere they are required most.

We hope you have a better idea of which tasks you should outsource to grow as a top real estate data aggregator. For more details about real estate data aggregation or public records aggregation, contact Actowiz Solutions now! Contact us for all your web scraping service and mobile app data scraping service requirements.